Recently there are many efforts that use representation learning techniques to address problems related to tabular data.
Sherlock [1]} and Sato [2]} utilized a feature based approach to learn vector representations for tables and columns.
TURL [3]} proposed to use a pre-trained language model for web table related tasks and to come up with benchmark datasets for several tasks.
And pre-trained language models have been widely applied to different table-related applications, including entity matching [4]}, [5]}, [6]}, column type detection [7]}, [8]}, and question answering [9]}, [10]}.
Our work follows this line of study and proposes the first solution that employs a pre-trained language model in a fully unsupervised way for the problem of table union search.
